Method and apparatus for preparing image representative data

ABSTRACT

A method for processing a digital mesh representing a three-dimensional subject is described. The method comprises utilizing smoothed splines to identify inflection points on the mesh for further processing.

FIELD OF THE INVENTION

This invention pertains to a system and method operating on datarepresentative of three-dimensional shapes, in general, and a system andmethod operating on data representative of three-dimensional headshapes, in particular.

BACKGROUND OF THE INVENTION

Cranial remodeling is utilized to correct for deformities in the headshapes of infants. Prior to the development of the Dynamic OrthoticCranioplasty^(SM) method of cranial remodeling by Cranial Technologies,Inc, the assignee of the present invention, the only viable approach forcorrection of cranial deformities was surgical correction of the shapeof the cranium. Dynamic Orthotic Cranioplasty^(SM) utilizes a treatmentprotocol in which the DOC BAND® cranial remodeling device is customproduced for each subject to be treated.

In the past, custom cranial remodeling devices were produced by firstobtaining a full size and accurate cast of the actual head shape of eachsubject. This cast was then modified to produce a second or desired headshape model. The second or desired head shape model is used to form thecranial remodeling band for the infant. In the past, the second ordesired shaped head shape model was obtained by manually modifying thefirst cast to form the desired shape model.

Cranial Technologies has maintained a “library” of the casts of the headcasts of infant's deformed heads and the corresponding models of thedesired corrected head shapes.

Cranial Technologies, Inc. continued its pioneering developments withits proprietary DSI® digital image capturing system and its DigitalSurface Imaging® methodology for the time efficient and safe imagecapture of three-dimensional full head images.

More specifically, the DSI® digital image capturing system was utilizedto capture DSI® digital data representative of digital images of eachcast of a deformed head and each corresponding model of the correctedhead shape and store the DSI® digital data for each digital image infirst and second databases, respectively. The first and second databaseswere utilized to train a neural network.

Cranial Technologies developed a system that utilized these first andsecond databases to automatically produces digital data representativeof a modified head shape from DSI® digital data representative of adeformed head. A processor operable with a neural network program trainsthe neural network program with the first plurality of first sets ofcaptured data stored in the first database and the second plurality ofsecond sets of captured data stored in the second database such that theneural network is trained to operate on a new set of captured data for afirst head shape to produce a corresponding modified head shape. In thatsystem, a support vector machine program is operated to train the neuralnetwork program.

In the Cranial Technologies system, captured data for a deformed head isprocessed utilizing Principal Component Analysis (PCA) to generate PCAdata representative of the deformed head. The PCA data is provided asinput to the neural network. The neural network processes the PCA datato provide data representative of a corresponding modified head shape.

The system developed by Cranial Technologies required the use of trainedoperators to manipulate the captured data for a variety of reasons.Clinical adaptations to accommodate individual subjects andcircumstances result in inconsistent orientations of the subject DSI®captured data files.

The trained operators view each DSI® captured data file of each subjectand manually reorient the viewed image to a predetermined orientation.After manual reorientation, the operator manually selects the portion ofthe DSI® image data files for further use, thereby eliminating regionsthat will not be utilized.

To achieve improved production efficiency and to maintain high qualityresults, an automated system and method that is operator independent orsubstantially operator independent is desired.

It is further desirable that a system and method are provided that willdirectly capture an image of a subject directly and process the directlycaptured image.

SUMMARY

In accordance with the principles of the invention, an improved methodand system are provided for the processing of digital three-dimensionalcaptured image representations of a subject. The improved method andsystem is an automated method and system that, in one embodiment issubstantially operator independent and in another embodiment is operatorindependent. The method and system automatically orients each digitalthree-dimensional image captured file such that the resulting file hasthe image of the subject oriented in accordance with a predeterminedorientation. The method and system automatically selectively crops orcuts each three-dimensional image captured file such that the resultingfile is limited to only a predetermined portion of the subject.

In an embodiment, the system operates on each new captured digital imagerepresentation or DSI® mesh of a subject to orient the new digital imagerepresentation consistent with digital image representations stored in alibrary database. After orienting, the system cuts or crops the DSI®mesh to obtain a corresponding cranial digital image representation ormesh.

The system automatically operates on each final digital imagerepresentation or DSI® mesh to produce a modified digital imagerepresentation or DSI® mesh for the corresponding subject.

In an embodiment, a method for processing representativethree-dimensional digital mesh representations captured from a livethree-dimensional subject is provided. Each of the representationscomprises an unstructured triangulated surface formed by the unit normaland vertices of triangles using a three-dimensional Cartesian coordinatesystem. The method comprises the steps of obtaining a first digital meshrepresentation of a live subject; utilizing smoothed splines to identifyinflection points; utilizing the splines and inflection points toidentify portions of the digital mesh for further processing.

The method further comprises utilizing at least one second spline toidentify a corresponding location of an upper orbit level of thesubject; and utilizing the selected second spline selection and saidinflection points to identify a mesh portion for processing.

The method further comprises automatically computing a scale factor;multiplying the mesh portion by the scale factor to produce a scaledmesh portion; and registering the scaled mesh to a reference mesh.

BRIEF DESCRIPTION OF THE DRAWING

The invention will be better understood from a reading of the followingdetailed description taken in conjunction with the drawing figures inwhich like designations are utilized to identify like elements, and inwhich:

FIG. 1 illustrates a representative three-dimensional image withphotographic overlay of a subject;

FIG. 2 illustrates the three dimensional image of FIG. 1 without thephotographic overlay;

FIG. 3 illustrates different views of the three dimensional image ofFIG. 2;

FIG. 4 illustrates steps utilized in one embodiment;

FIG. 5 illustrates further steps utilized in an embodiment;

FIG. 6 illustrates a vertex cloud for determination of a reference axisfor an image of FIG. 1;

FIG. 7 illustrates the views of FIG. 3 with the image re-oriented to areference axis;

FIG. 8 illustrates the location of a cropping plane on the views of FIG.7;

FIG. 9 illustrates the images of FIG. 8 after a first crop;

FIGS. 10 and 11 illustrate the location of Gaussian weighted centers ofthe head and chest portions of the images of FIG. 9;

FIG. 12 illustrates the position of a second cropping plane;

FIG. 13 illustrates a view of the three-dimensional image after a secondcropping;

FIG. 14 illustrates a further methodology steps;

FIG. 15 a cropping configuration; and

FIG. 16 is block diagram of a system.

DETAILED DESCRIPTION

A library of hundreds of infant head casts and corresponding modifiedmodels has been maintained at the assignee of the present invention andthis library of actual head casts and the corresponding modified modelsis believed to be a unique resource. It is this unique resource that wasutilized to provide databases for developing the method and apparatus ofthe prior system.

Cranial Technologies, Inc. developed an image capture technology thathas successfully replaced the traditional casting process. Thistechnology referred to as the DSI® technology captures a 360-degreeglobal image capture DSI® and provides improved surface detail overlarger regions of the patient than achieved through casting.

Cranial Technologies utilized the DSI® system to capturethree-dimensional images of plaster casts of patients' heads to developa database that in turn was utilized to train a support vector machine.

Applicant recognized that the improved initial digital data recordprovided by the DSI® system presents an opportunity to provide animproved database, method and system in which three-dimensional digitalimage data captured directly from live subjects may be used to develop anew database and an improved system and methodology. Applicant hasdeveloped a new database, a new system, and new methodologies describedherein that operate directly from image data captured directly from livesubjects.

The DSI® system generates an image data file that is a digital mesh thatrepresents the captured 360-degree global image. This image referred toas a DSI® mesh may be viewed on a monitor. In the past each DSI® meshwas manipulated by an operator.

In developing the database, system, and method of the invention, DSI®image data files captured directly from approximately 3,000 livesubjects were utilized. These unaltered files, i.e., files that wereneither oriented nor cropped, were selected to be representative files.

The DSI® image data files captured directly from subjects were utilizedto construct a database utilized as described hereinafter.

U.S. Pat. No. 7,127,101 issued Oct. 24, 2006; U.S. Pat. No. 7,142,701issued Nov. 28, 2006; U.S. Pat. No. 7,162,075 issued Jan. 9, 2007; U.S.Pat. No. 7,177,461 issued Feb. 13, 2007; U.S. Pat. No. 7,227,979 issuedJun. 5, 2007; U.S. Pat. No. 7,242,798 issued Jul. 10, 2007; U.S. Pat.No. 7,245,743 issued Jul. 17, 2007; U.S. Pat. No. 7,280,682 issued Oct.9, 2007; and U.S. Pat. No. 7,305,369 issued Dec. 4, 2007 are allassigned to the assignee of the present application and the disclosurescontained in each of the patents are expressly incorporated herein byreference.

U.S. patent application Ser. No. 12/383,198 filed Mar. 20, 2009 andpublished as Publication No. 2010/0239135A1 on Sep. 23, 2010; and Ser.No. 12/798,076 filed Mar. 29, 2010 and published as Publication No.2010/0238273A1 published on Sep. 23, 2010 are all assigned to theassignee of the present application and the disclosures contained ineach of the applications as published are expressly incorporated hereinby reference.

Turning now to FIGS. 1 and 2, a typical DSI® data file 10 captured froma live subject. FIG. 1 shows an operator viewed image 100 of data file10 with a photographic overlay, and FIG. 2 illustrates a viewed image1000 of data file 10 without photographic overlay. For a variety ofreasons, the orientations of data files 10 and the corresponding images100, 1000 vary significantly from subject to subject. Also, as isapparent from FIGS. 1 and 2, the captured image data includes not onlythe head 101, but the chest 103 of the subject and if the subject isheld in position, the hands 105 of the holder of the subject.

Turning to FIG. 3, a typical screen-shot displaying different views of adata file 10 is shown. Screen-shot 300 includes a right side view 301,top view 303, rear view 305 and left side view 307.

Captured DSI® data for different subjects are not aligned with eachother. Accordingly captured DSI® data for a plurality of subjects cannotsimply be averaged together. The captured DSI® data for differentsubjects must be properly scaled and oriented before averaging can beeffective.

One method for registering images and digital records of anthropologicalartifacts is known as the “Procrustes method”. It applies where a groupof similar but individually unique items needs to be consistentlydescribed or processed. The Procrustes method as it is referred to inthe scientific literature is simply resizing and alignment of eachelement in the database to match the orientation and size of the averageelement of the database. The fundamental difficulty encountered is thatthe average element of the database is not known before the alignmentprocess begins.

In an embodiment of the invention, a “Procrustes” type of registering ofdata files in a database is provided.

As shown in FIG. 4, database alignment shown at step 407 is preceded byfirst selecting as a reference mesh, a “typical” or “reference” subjectDSI® data file 403 from the database and registering all of the otherDSI® data file to the “reference” file at step 405.

Registration occurs by changing the seven parameters on the DSI® datamesh until a metric measuring the alignment of the two meshes isoptimized. Registration optimization is obtained in two separate steps:a coarse registration followed by a fine registration. The coarseregistration employs a robust metric and brings the two objects closeenough together so that the sensitive metric employed by the fineregistration can succeed to produce a more exact result. After thisinitial alignment is performed, all DSI® data files in the databasebecome more closely aligned.

To simplify the math the six orientation parameters of a reference meshare all set to zero. As a consequence, only the six parameters for theDSI® mesh are needed to align the two meshes. Adding a singlemagnification or scale parameter then brings the total number ofparameters to seven. Magnification of the Reference is taken as 1.0.Orientation of DSI® data meshes to the reference mesh is accomplished byspecifying six parameters: one translation (distance from the origin)for each of the three coordinate axes and one rotation around eachcoordinate axis. Aligning two DSI® meshes requires specifying the sixparameters for each DSI® mesh so that the two DSI® meshes will be in thesame spatial location and orientation.

Database alignment 407 is then performed by first averaging the moreclosely aligned files at step 409 to produce a new reference file. EachDSI® data file is then re-aligned to the new reference file at step 413.The result is that all the files are brought into even better alignmentbecause the new average was more typical than the original DSI®reference. This averaging and re-alignment processing steps 409, 411 arerepeated the reference DSI® data file produced does not changesignificantly with repeated processing.

Final orientation of DSI® data meshes is achieved after automatedalignment and cutoff of each new DSI® data mesh as described below. Analignment algorithm registers each new DSI® data mesh to the databasereference independent of the orientation of the reference. By adjustingthe orientation of the reference, each new DSI® data mesh isautomatically oriented as well as registered. The final orientation canbe adjusted at any time.

Although human vision systems easily recognize general similaritiesbetween objects, mathematical registration does not. We developed anapproach to automatically provide registration.

In the following description of registration of two meshes, the first ofthe two meshes is referred to as the “library” or “reference” mesh andthe second mesh is referred to as the DSI® mesh.

In a first embodiment of the invention, a system and method are providedthat operate on a three-dimensional digital image of a subject that isin a predetermined format. The system and method automatically crop andorientate the digital image to be consistent with a library reference.Four crops of the digital mesh are automatically provided to yield adigital mesh that is stored in a database and that is used for furtherprocessing.

In an alternate embodiment of the invention, at least some of the cropsand orientating are provided with operator control and/or intervention.

The methodology utilized various embodiments is first summarized belowwith respect to the method steps of FIG. 5 and a representative vertexpoint cloud 600 for the subject of FIGS. 1 and 2 shown in FIG. 6.

In an initial step 501, extraneous information, i.e., stray polygons andvertices, is removed. Removal of unattached vertices and other meshelements is achieved using a commercially available software function onan adjacency matrix defined by the DSI® mesh. This operation leavesintact only the largest “connected” section of the mesh.

After removal of the extraneous information, two points are selected forthe vertex point cloud 600 of the subject at step 503 as shown in FIG.6. The median M of vertex point cloud 600 is determined. The median M ofvertex point cloud 600 is independent of the orientation of cloud 600.For the DSI® captured data files, the median M is almost alwaysapproximately in the center of the chest cavity.

The furthest point C away from the median but lying such that both itsz-axis coordinate and y-axis coordinate are positive is identified.Point C is on the upper cranium.

The line L joining the median M and this furthest point C is taken to bea new z-axis Z0 at step 505. The other axes, i.e., x-axis and y-axis,are computed easily since they are orthogonal to z-axis Z0.

Once axis Z0 is identified, a first crop is performed at step 507 toremove all portions of the image that are more than a firstpredetermined radial distance R away from axis Z0. The radial distanceselected in the illustrative embodiment is selected to be 150 cm.

Axis Z0 is utilized to orient image 100 consistent with the reference.The reorientation produces the reoriented images as shown in FIG. 7.

A second crop is performed at step 509 as shown in FIG. 5. A plane P isselected at a second predetermined distance from the second point C asshown in FIG. 8. Plane P is selected to be perpendicular to axis Z0. Allportions of the DSI® data mesh 1000 that lie below plane P are croppedout or removed.

The resulting data mesh 900 shown in FIG. 9 comprises the cranium 101and the upper chest portion 103, 105 of the subject DSI® data mesh 1000.This final “cranial mesh” 900 contains fewer vertices and fewertriangles than the original DSI® mesh.

Turning back to FIG. 5, at step 511 the cranial and upper neck portionof the DSI® data mesh is separated from the remainder of the mesh. Apredetermined algorithm is utilized to separate the cranial and upperneck portion of the image from the remainder of the mesh. Thepredetermined algorithm utilized in the embodiment of the invention is amixture of Gaussians (MOG) algorithm.

The remaining mesh containing primarily the chest and cranial regions,is analyzed using MOG to identify each vertex as lying in one of twoclasses. One class normally contains only the upper neck and cranialregion, the other class has the rest of the chest mesh. Thisneck/cranial region is entered into a coarse registration. After using a“Procrustes” function on the geodesically determined vertices, allfurther registrations are based on predetermined points selected in thecranial region of the “chest mesh”. Using the chest mesh in laterregistration rounds allows more of the neck region to enter theregistration if needed, but using only the cranial region for theinitial 512 vertices better concentrates those and all later selectionswithin the cranium.

FIGS. 10 and 11 illustrate the locations of a calculated MOG craniumcenter G1 and a calculated chest MOG point G2. Utilizing MOG points G1,G2, a crop plane C0 is determined as shown in FIG. 12.

DSI® data mesh 900 is cropped to remove the mesh portion lying belowcrop plane C0 producing the DSI® data mesh 1300 shown in FIG. 13.

Turning back to FIG. 5, the resulting DSI® data mesh image 1300 is thenscaled to a library reference mesh based upon the shape of the craniumutilizing Frobenius metrics to determine a scale factor to the libraryreference mesh as indicated at step 513.

Following scaling, the resulting DSI® data cranial mesh is registered atstep 515 to the library reference mesh utilizing a two-steptranslational registration using least squares followed by mutualinformation. The resulting DSI® data cranial mesh 1300 is stored in adatabase for further processing.

In a second embodiment of the invention, the cranial mesh is furtheroperated on to identify those portions of the mesh 1300 that are ofparticular interest for further processing.

In the second embodiment, further cropping of the image mesh isprovided.

In the particular application of the system and method of the invention,the portion of the subject below the bottom of the ear lobes is not ofrelevance.

In particular, portions of the DR® data cranial mesh that are notnecessary for further processing are cropped off the image mesh.

Turning now to FIG. 14, the additional steps that are utilized in theembodiment of the invention are described.

At step 1401 the DSI® data cranial mesh 1300 is obtained from thedatabase.

Key inflection points are identified on the mesh image to locate theorbits of the subject and to identify the bottoms of the ears of thesubject. Key inflection points in the embodiment are determined by firstslicing the DSI® data cranial mesh through coronal and sagittal planesas indicated at step 1403. Smooth splines are fit to posterior regionsat step 1405. Inflection points are identified representing the bottomsof the ears 1301 and the neck/cranial transitions 1303 at step 1407.

A spline is fit to the anterior sagittal intersection of the planes toidentify the location of the upper orbit level 1305 at step 1409.

After key inflection points 1301, 1303, 1305 are identified, theinflection points are utilized to identify relevant portions of the DSI®data cranial mesh 1300 at step 1411.

The identification of the bottoms of the ears is utilized to identify afurther crop plane that is used to exclude that portion of the DR® datacranial mesh below the plane to exclude or crop off the lower neckregion from DSI® data cranial mesh at step 1413 to produce the digitalmesh 1500 shown in FIG. 15.

Subsequent to cropping the DSI® data cranial mesh 1300, Frobeniusscaling is again applied to produce a cranial mesh. Frobenius scaling isaccomplished by computing a Frobenius norm at step 1415 and thencomputing a scale factor at step 1417. The DSI® data cranial mesh isthen multiplied by the scale factor at step 1419.

After the Frobenius scaling, the cranial mesh is registered to thereference mesh at step 1421 by utilizing translational registrationusing least squares. A “Procrustes” function is used to apply leastsquares with iterative closest points computed by normal shooting, i.e.,bed of nails, at step 1423. The Procrustes function step is followed bymutual information (MI) with pattern search optimization at step 1425.All rotational degrees of freedom remain unchanged, only thetranslational degrees are optimized.

After isolating cranium mesh CM and defining a new z-axis Z0, additionalcoarse registration is performed using an Iterative Closest Points (ICP)algorithm. The ICP algorithm operates by selecting a set of points onthe reference and locating the closest set of matching points on thecranial mesh. A set of transformations is applied and the registrationquality metric is computed for each of the transformations. Once theoptimal transformation according to the metric is identified and appliedto the cranial mesh, the matching of cranial mesh points to those on thereference is repeated. Because the cranial mesh location has beentransformed, the new matching set is different than the previous matchedset and so the transformation optimization is again optimized and theclosest matching set of points identified. These iterations continueuntil the transformations are acceptably small.

Turning now to FIG. 16, system 800 utilizes the methodologies describedabove. System 800 comprises a computer or processor 801 that processesDSI® files received from DSI® system 802. A plurality of DSI® files arestored in database 804. The database 804 files are used to train aSupport Vector Machine (SVM) 817. Support Vector Machine 817 is anapplication that processes each DSI® file to output a properly orientedand “modified” stereo lithography (STL) standard file replicating amodified shape that would have previously been produced by a trainedexpert.

STL is a file format native to stereo lithography CAD software that iscommercially available. Su, files describe only the surface geometry ofa three dimensional object without any representation of color, textureor other common CAD model attributes. An STL file describes a rawunstructured triangulated surface by the unit normal and vertices of thetriangles using a three-dimensional Cartesian coordinate system

System 800 automatically processes digital image representationsrepresentative of a subject head shape. System 800 comprises a databaselibrary 804 of a first plurality of first digital image representationsof subject head shapes captured directly from live subjects, and asecond plurality of second digital image representations ofcorresponding modified head shapes. System 800 includes processor 801that further comprises a support vector machine application 817.Database library 804 is used to provide the plurality of said first andsecond digital image representation to processor 801 to train supportvector machine 817 to operate on new digital image representations.

System 800 receives a new digital image representation file or DSI® meshof a subject head shape from DSI® system 802. Support vector machine 817operates on the DSI® mesh to generate a corresponding new second digitalimage representation replicating a corresponding modified head shape andstores each new digital image representation file and the correspondingnew second digital image representation file in database 804.

In accordance with the methodology described hereinabove, processor 801operates on a raw file received from DSI® 802. Processor 801 removes allvertices, polygons, or other mesh elements that are not attached to thesubject. Processor 801 analyzes the resulting point cloud of theretained mesh using k-means. This provides two “centers”, one for thevertices labeled to be in the upper mesh and the other for verticeslabeled to be in the lower mesh. A line joining the upper and lowercenters defines an initial vertical z-axis for the patient. A patienty-axis is computed as the cross product of this patient z-axis with theoriginal x-axis provided by the digitizer. A new patient x-axis isfinally computed as the cross product of the patient y-axis and z-axis.The mesh is rotated into this initial “patient coordinate system”. Inthe patient coordinate system a “chest cutoff” is applied to produce the“chest mesh”. For this, mesh elements less than 250 mm from the highestpoint of the mesh and lying within 150 mm of the z-axis are retained. Amixture of Gaussians (MOG) algorithm is then applied to separate thecranial and upper neck region from the rest of the chest mesh.

The median of the remaining cranial mesh is subtracted from each vertexin order to center at a new origin of coordinates. This median iscomputed using an area-weighted statistical sampling of the mesh. Thissampled median approach overcomes non-uniformly spaced verticesproduced.

Processor 801 then operates on each new digital image representation orDSI® mesh to orient said new digital image representation consistentwith the digital image representations or DSI® mesh information storedin database 804. After orienting, processor 801 cuts or crops the DSI®mesh to obtain a corresponding cranial mesh.

After obtaining a corresponding cranial mesh, system 800 processes thecranial mesh to generate a new digital image representation or modifiedmesh representative of a desired head shape.

System 800 updates database 804 by storing each new first digital imagerepresentation or DSI® mesh in the database library 804 with the firstplurality of first digital image representations and storing eachcorresponding new second digital image representation.

System 800 utilizes the updated database 804 to retrain support vectormachine 817.

Independent Component Analysis (ICA) application program 811 initiatesan ICP algorithm by selecting predetermined number vertices geodesicallyspaced on the reference model and then mathematically projected onto aunit sphere, i.e., a sphere centered at zero and having a radius of 1.0.Vertices from the cranial mesh are also projected onto the unit sphereand those lying closest to the set of the predetermined number ofvertices projected from the reference model were selected to initiatethe ICP algorithm.

The metric used to assess registration quality is sensitive enough todistinguish between small movements, but robust enough to achieve goodresults with the large variety of shapes presented by the raw cranialmeshes.

After all subject files in database 804 are registered to the referencemesh, the average of the cranial meshes is taken to generate an updatedreference mesh and continue the Procrustes averaging

Turning back to FIG. 14, shapes computed by ICA are applied to the DSI®data cranial mesh at step 1427. A predetermined set of 128 independentcomponents, IC, was found to allow very satisfactory representation.

The cranial mesh represented using ICA shapes is applied to a supportvector machine (SVM) 817. SVM 817 uses the cranial mesh IC's as inputand computes modified IC's as its outputs at step 1429.

The modified IC's define a cranial mesh for a modified head shape. Thecranial mesh for the modified head shape is utilized to fabricate acranial device.

To prepare for the first rounds of Iterated Closest Points (ICP) twopreliminary steps are performed. A search tree for the cranial mesh isbuilt and a “unit sphere matching” is performed. The search tree speedsthe process of identifying which points in the cranial mesh lie closestto those in the reference mesh set. Given the index of a point or set ofpoints in the reference mesh set, ICP requires identifying the index ofthe closest point in the cranial mesh. This tree simply speeds thesearches for the closest points. The “unit sphere matching” is done toinitiate the ICP.

Each vertex of the re-centered DSI® mesh is projected onto the unitsphere by dividing the vertex vector by its own magnitude. A set of 512vertices is established on the reference mesh. That set of vertices isgeodesically spaced on the reference mesh and their indices are storedin database 804. Projecting them onto the same unit sphere as there-centered cranial mesh allows identifying the set of cranial meshvertices that are closest to the geodesic set from the reference mesh.This matched set and the search tree are used to do a predeterminednumber of rounds of ICP using a “Procrustes” function.

Fine registration differs from the coarse registration in threesignificant ways. First, the reference mesh is treated as a surfacerather than just a collection of geodesically spaced vertices. Second,the set of vertices used in the cranial mesh do not change; they are theones from the final match achieved in the coarse registration. The thirddifference is that only MI optimization is done, no preliminary ICP isapplied.

Processor 801 averages all of the cranial meshes together to establish areference mesh.

Once Processor 801 aligns all of the cases in database 804, processor801 computes the trimmed mean of each vertex. The trimmed mean throwsaway the most extreme 30 percent of the cases at each vertex andcomputes a uniformly weighted average of the remaining cases.

Processor 801 applies averaging separately to the x, y, and zcoordinates to generate a “right side” flattened average mesh. The rightside mesh is then mathematically mirrored about its x-axis and averagedwith itself to create a symmetric reference mesh.

Processor 801 uses the symmetric reference mesh to compute otherinformation stored in database 804 to define the coordinate system for a“cone” used in a final cropping. This crop is achieved using a truncatedand inverted cone 1501 as shown in FIG. 15. The axis z of the cone 1501is aligned with the vertical axis and its wall is at 45 degrees from thevertical. The mesh elements lying above a horizontal plane P0 disposedorthogonal to cone axis Z and the inside inverted cone 1501 are retainedas the final mesh; those outside of that region are discarded.

The method and system of the invention provide orientation independenceof the subject. Orientation independence refers to achieving theforeground/background separation with no requirements on how the subjectis oriented. It includes effective methods to ignore the irrelevantlimbs, stools, and even pacifiers.

The invention has been described in terms of illustrative embodiments.It will be apparent to those skilled in the art that various changes andmodifications can be made to the illustrative embodiments withoutdeparting from the spirit or scope of the invention. It is intended thatthe invention include all such changes and modifications. It is alsointended that the invention not be limited to the illustrativeembodiments shown and described. It is intended that the invention belimited only by the claims appended hereto.

1. A method for processing digital mesh images representations ofthree-dimensional subjects, said method comprising: slicing said meshrepresentation with a frontal imaginary plane that divides the meshrepresentation into anterior and posterior sections; slicing said meshrepresentation with a sagittal imaginary plane extending verticallythrough said mesh representation dividing said mesh representation intoleft and right sections, said frontal imaginary plane intersecting saidsagittal imaginary plane; and using a spline function to fit smoothedsplines to selected ones of said anterior and posterior sections andsaid left and right sections.
 2. A method in accordance with claim 1,comprising: utilizing said smoothed splines to identify inflectionpoints.
 3. A method in accordance with claim 2, comprising: utilizingsaid inflection points to represent the bottoms of ears
 4. A method inaccordance with claim 3, comprising: utilizing at least some of saidinflection points to represent a neck/cranial transition.
 5. A method inaccordance with claim 2, comprising: utilizing at least some of saidinflection points to represent a neck/cranial transition.
 6. A method inaccordance with claim 1, comprising: fitting a second spline to identifylocation of an upper orbit level
 7. A method in accordance with claim 1,comprising: utilizing a plurality of second splines to identify acorresponding plurality of locations of an upper orbit level selectingone of said plurality of second splines as the location of the upperorbit level.
 8. A method in accordance with claim 2, comprising:utilizing a plurality of second splines to identify a correspondingplurality of locations of an upper orbit level selecting one of saidplurality of second splines as the location of the upper orbit level. 9.A method in accordance with claim 8, comprising: utilizing saidinflection points and one of said second spline selections to identify amesh portion of said mesh representation for processing.
 10. A method inaccordance with claim 9, comprising: computing a scale factor usingrepeated random sampling of said mesh portion.
 11. A method inaccordance with claim 9, comprising: randomly locating a predeterminednumber of vertices on said mesh portion by uniformly sampling said meshportion; computing a Frobenius norm of said vertices; repeating saidrandomly locating step and said computing a Frobenius norm step apredetermined number of times on said mesh portion to produce aplurality of Frobenius norms; and utilizing said plurality of Frobeniusnorms to determine a median Frobenius norm.
 12. A method in accordancewith claim 11, comprising: computing a scale factor as the ratio of theFrobenius norm of a reference mesh and said median Frobenius norm; andmultiplying said mesh portion by said scale factor to adjust the size ofsaid mesh portion to match that of a standard grid to thereby produce ascaled mesh.
 13. A method in accordance with claim 12, comprising:registering said scaled mesh to a library reference mesh.
 14. A methodin accordance with claim 13, comprising: performing said registeringstep utilizing a two-step translational registration.
 15. A method inaccordance with claim 14, wherein: said two-step translationregistration step comprises using a least squares method.
 16. A methodin accordance with claim 14, comprising: said two-step translationregistration step comprises utilizing a “Procrustes” function to applysaid least squares method with iterative closest points computed bynormal shooting such that said scaled mesh is aligned to said libraryreference using the shape of said scaled mesh.
 17. A method inaccordance with claim 16, wherein: said two-step translationregistration step comprises: utilizing a mutual information (MI) withpattern search optimization method.
 18. A method in accordance withclaim 14, wherein: said two-step translation registration stepcomprises: utilizing mutual information (MI) with pattern searchoptimization method to optimize an MI position.
 19. A method forprocessing digital mesh representations representative ofthree-dimensional subjects, said method comprising: obtaining a firstdigital mesh representation of a subject; and utilizing smoothed splinesto identify inflection points.
 20. A method in accordance with claim 19,comprising: utilizing said inflection points to represent the bottoms ofears
 21. A method in accordance with claim 19, comprising: utilizing atleast some of said inflection points to represent a neck/cranialtransition.
 22. A method in accordance with claim 19, comprising:utilizing said inflection points to identify a mesh portion forprocessing.
 23. A method for processing digital mesh representationsrepresentative of three-dimensional subjects, said method comprising:obtaining a first digital mesh representation of a subject; andutilizing at least one smoothed spline to identify a correspondinglocation of an upper orbit level.
 24. A method in accordance with claim23, comprising: utilizing a plurality of second splines to identify acorresponding plurality of locations of an upper orbit level; andselecting one of said plurality of second splines as the location of theupper orbit level.
 25. A method in accordance with claim 24, comprising:utilizing said selected second spline selection to identify a meshportion for processing.
 26. A method in accordance with claim 23,comprising: utilizing smoothed splines to identify inflection points.27. A method in accordance with claim 26, comprising: utilizing saidinflection points to identify a mesh portion for processing.
 28. Amethod for processing a digital mesh representing a three-dimensionalsubject, said method comprising: utilizing smoothed splines to identifyinflection points on said mesh; utilizing one smoothed spline toidentify a corresponding location of an upper orbit level on said mesh;and utilizing said upper orbit level and said inflection points toidentify a mesh portion for processing.
 29. A method in accordance withclaim 28, comprising: computing a scale factor for said mesh usingrepeated random sampling of said mesh portion.
 30. A method inaccordance with claim 28, comprising: randomly locating a predeterminednumber of vertices on said mesh portion by uniformly sampling said meshportion; computing a Frobenius norm of said vertices; repeating saidrandomly locating step and said computing a Frobenious norm step apredetermined number of times on said mesh portion to produce aplurality of Frobenius norms; and utilizing said plurality of Frobeniusnorms to determine a median Frobenius norm.
 31. A method in accordancewith claim 30, comprising: computing a scale factor as the ratio of theFrobenius norm of a reference mesh and said median Frobenius norm; andmultiplying said mesh portion by said scale factor to adjust the size ofsaid mesh portion to match that of a standard grid to thereby produce ascaled mesh.
 32. A method in accordance with claim 31, comprising:registering said scaled mesh to a library reference mesh.
 33. A methodfor processing digital mesh representations representative ofthree-dimensional subjects, said method comprising: obtaining a firstdigital mesh representation of a subject; utilizing smoothed splines toidentify inflection points on said mesh; utilizing at least one smoothedspline to identify a corresponding location of an upper orbit level;utilizing said selected second spline selection and said inflectionpoints to identify a mesh portion for processing; computing a scalefactor; and multiplying said mesh portion by said scale factor toproduce a scaled mesh portion.
 34. A method in accordance with claim 33,comprising: registering said scaled mesh to a library reference mesh.35. A method in accordance with claim 34, comprising: performing saidregistration step utilizing a two-step translational registration.
 36. Amethod in accordance with claim 35, wherein: said two-step translationregistration step comprises using a least squares method.
 37. A methodin accordance with claim 35, wherein: said registration step comprisesutilizing a “Procrustes” function to apply a least squares method withiterative closest points computed by normal shooting such that saidscaled mesh is aligned to said library reference mesh using the shape ofsaid scaled mesh portion.
 38. A method in accordance with claim 34,wherein: said two-step translation registration step comprises:utilizing mutual information (MI) with pattern search optimizationmethod.
 39. A method in accordance with claim 33, comprising: aligningsaid scaled mesh portion to a reference mesh using the shape of saidscaled mesh portion